Learning Activation Functions in Deep Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis is dedicated to my beloved parents, Ahmadreza and Sholeh, who are my first teachers and always love me unconditionally.This work is also dedicated to my love, Arash, who has been a great source of motivation and encouragement during the challenges of graduate studies and life.Des mthodes et des algorithmes pour dvelopper ces fonctions d'activation adaptatives sont discuts.En outre, une petite variante de MLP (Multi Layer Perceptron) et un modle CNN (Convolutional Neural Network) applicant nos fonctions d'activation proposes sont utiliss pour prdire l'intention des utilisateurs selon les donnes d'URL.Quatre jeux de donnes diffrents ont t choisis, appel les donnes simules, les donnes MNIST, les donnes de revue de film, et les donnes d'URL pour dmontrer l'effet de slectionner diffrentes fonctions d'activation sur les modles MLP et CNN proposs.vi
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it